240 citations · 571 across the 28 of their papers we have counts for
9 papers · 1 filter
DPack: Efficiency-Oriented Privacy Budget Scheduling
Pierre Tholoniat, Kelly Kostopoulou, Mosharaf Chowdhury +4
Machine learning (ML) models can leak information about users, and differential privacy (DP) provides a rigorous way to bound that leakage under a given budget. This DP budget can…
Auxo: Efficient Federated Learning via Scalable Client Clustering
Jiachen Liu, Fan Lai, Yinwei Dai +3
Federated learning (FL) is an emerging machine learning (ML) paradigm that enables heterogeneous edge devices to collaboratively train ML models without revealing their raw data to…
Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training
Jie You, Jae-Won Chung, Mosharaf Chowdhury
Training deep neural networks (DNNs) is becoming increasingly more resource- and energy-intensive every year. Unfortunately, existing works primarily focus on optimizing DNN traini…
Orloj: Predictably Serving Unpredictable DNNs
Peifeng Yu, Yuqing Qiu, Xin Jin +1
Existing DNN serving solutions can provide tight latency SLOs while maintaining high throughput via careful scheduling of incoming requests, whose execution times are assumed to be…
Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs
Sanjay Sri Vallabh Singapuram, Fan Lai, Chuheng Hu +1
The need to train DNN models on end-user devices (e.g., smartphones) is increasing with the need to improve data privacy and reduce communication overheads. Unlike datacenter serve…
TPP: Transparent Page Placement for CXL-Enabled Tiered-Memory
Hasan Al Maruf, Hao Wang, Abhishek Dhanotia +7
The increasing demand for memory in hyperscale applications has led to memory becoming a large portion of the overall datacenter spend. The emergence of coherent interfaces like CX…
Elastic Model Aggregation with Parameter Service
Juncheng Gu, Mosharaf Chowdhury, Kang G. Shin +1
Model aggregation, the process that updates model parameters, is an important step for model convergence in distributed deep learning (DDL). However, the parameter server (PS), a p…
Treehouse: A Case For Carbon-Aware Datacenter Software
Thomas Anderson, Adam Belay, Mosharaf Chowdhury +2
The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwid…
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing
Yiding Wang, Decang Sun, Kai Chen +2
Training deep neural networks (DNNs) is time-consuming. While most existing solutions try to overlap/schedule computation and communication for efficient training, this paper goes…